Next Article in Journal
Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests
Next Article in Special Issue
Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta
Previous Article in Journal
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
Previous Article in Special Issue
Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm
Article Menu
Issue 7 (April-1) cover image

Export Article

Open AccessArticle

Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Land Quality, Ministry of Land and Resources of the China, Beijing 100083, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 888;
Received: 19 February 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 11 April 2019
PDF [109865 KB, uploaded 12 April 2019]
  |     |  


The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images. View Full-Text
Keywords: agriculture; high spatial resolution images; semantic labeling agriculture; high spatial resolution images; semantic labeling

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Du, Z.; Yang, J.; Ou, C.; Zhang, T. Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method. Remote Sens. 2019, 11, 888.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top